Local thermal sensation prediction method for joints based on physiological parameters

With the improvement of living standards, more and more people pay attention to local thermal comfort, especially the thermal sensation of joints. Physiological parameters and environmental parameters are the direct internal and extrinsic factors influencing human thermal sensation vote (TSV), respectively. The existing local thermal sensation models based on physiological parameters and environmental parameters can predict the local thermal sensation, but the prediction accuracy of joint local thermal sensation is not high. Therefore, in this paper, a local thermal sensation model suitable for joints is firstly established based on the local skin temperature, and the galvanic skin response (GSR) and the decision tree model are presented to improve the prediction accuracy of the local thermal sensation model. The results show that the local thermal sensation model based on local skin temperature and GSR can predict the TSV of joints and other locally sensitive locations, and the decision tree method can be used to determine the correction direction of the deviation between the predicted value and the actual value. When the deviation between the predicted value of the model and the actual value of TSV is within ±0.5, the average prediction accuracy is more than 80%, and the prediction accuracy of the model with GSR correction is 9.1% higher than that of the model constructed with only a single local skin temperature. The model can accurately predict the local thermal sensation of the joints, thereby improving the local thermal comfort of the human body.